【摘要】 为了破解用户口令并获取用户隐私信息，口令猜测工具应运而生.基于规则的口令猜测工具虽猜测成功率较高，但制定规则非常耗时且需要一定的专业知识.基于深度神经网络的口令猜测工具则需要大量的训练数据集来训练模型.基于此，本文提出了（Sequence Generative Adversarial Network Password, SeqGANPass），利用序列生成式对抗网络，针对口令数据集执行数据预处理操作，经由多轮对抗性训练过程训练口令生成器，以生成高质量的猜测口令.即使没有任何先验知识，SeqGANPass仍可以通过小规模训练集来实现口令破译.同时我们发现使用SeqGANPass可以大大提高基于规则的口令猜测工具的有效性.在实验中，我们与当前的主流口令猜测工具进行比较，如John the Ripper,Hashcat,Markov Model，上下文无关文法（Probabilistic Context Free Grammars,PCFG）,FLA(Fast, Lean, and Accurate)和PassGAN等.实验表明，SeqGANPass的匹配率优于这些主流的口令猜测工具.更多还原

【Abstract】 In order to crack the user’s password to achieve the purpose of obtaining user’s private information, password guessing tools also came into being. Although state-of-the-art rule-based attacks work achieve high attack success rate, the collection of rules is time consuming and needs expertise. Deep neural network-based attacks require amounts of datasets to achieve a good result. In this paper, we propose sequence generative adversarial network password(SeqGANPass), which uses sequence generative adversarial nets, conducts data preprocessing operations on the password datasets, to generate high-quality passwords. SeqGANPass can implement password cracking under a small scale of training set even without any prior knowledge. Furthermore, we show that SeqGANPass can greatly improve the effectiveness of rule-based attacks. Our experiments show that SeqGANPass outperforms most state-of-the-art password guessing methods, i.e., John the Ripper, Hashcat, Markov model, probabilistic context free grammars(PCFG), FLA(Fast, Lean, and Accurate), and PassGAN in matching rate.更多还原